Improving community detection via community association strength scores
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866909635884089344 |
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| author | Barrett, Jordan DeWolfe, Ryan Kamiński, Bogumił Prałat, Paweł Smith, Aaron Théberge, François |
| author_facet | Barrett, Jordan DeWolfe, Ryan Kamiński, Bogumił Prałat, Paweł Smith, Aaron Théberge, François |
| contents | Community detection methods play a central role in understanding complex networks by revealing highly connected subsets of entities. However, most community detection algorithms generate partitions of the nodes, thus (i) forcing every node to be part of a community and (ii) ignoring the possibility that some nodes may be part of multiple communities. In our work, we investigate three simple community association strength (CAS) scores and their usefulness as post-processing tools given some partition of the nodes. We show that these measures can be used to improve node partitions, detect outlier nodes (not part of any community), and help find nodes with multiple community memberships. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_17817 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improving community detection via community association strength scores Barrett, Jordan DeWolfe, Ryan Kamiński, Bogumił Prałat, Paweł Smith, Aaron Théberge, François Social and Information Networks Community detection methods play a central role in understanding complex networks by revealing highly connected subsets of entities. However, most community detection algorithms generate partitions of the nodes, thus (i) forcing every node to be part of a community and (ii) ignoring the possibility that some nodes may be part of multiple communities. In our work, we investigate three simple community association strength (CAS) scores and their usefulness as post-processing tools given some partition of the nodes. We show that these measures can be used to improve node partitions, detect outlier nodes (not part of any community), and help find nodes with multiple community memberships. |
| title | Improving community detection via community association strength scores |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2501.17817 |